




from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
	from ... import FeatureExtractionMixin, TensorType

snake_case_  :      Tuple		=		logging.get_logger(__name__)

snake_case_  :      int		=		{
    "openai/imagegpt-small": "",
    "openai/imagegpt-medium": "",
    "openai/imagegpt-large": "",
}





class 					snake_case_   (     _lowerCAmelCase	):



				'''simple docstring'''
				lowerCamelCase       =  '''imagegpt'''
				lowerCamelCase       =  ['''past_key_values''']
				lowerCamelCase       =  {
				    '''hidden_size''': '''n_embd''',
				    '''max_position_embeddings''': '''n_positions''',
				    '''num_attention_heads''': '''n_head''',
				    '''num_hidden_layers''': '''n_layer''',
				}





				def __init__(      self							:   List[str]      ,							__magic_name__							:   Dict=512 + 1      ,							__magic_name__							:   Union[str, Any]=32 * 32      ,							__magic_name__							:   Dict=512      ,							__magic_name__							:   int=24      ,							__magic_name__							:   Any=8      ,							__magic_name__							:   Union[str, Any]=None      ,							__magic_name__							:   int="quick_gelu"      ,							__magic_name__							:   int=0.1      ,							__magic_name__							:   List[str]=0.1      ,							__magic_name__							:   Dict=0.1      ,							__magic_name__							:   str=1e-5      ,							__magic_name__							:   str=0.02      ,							__magic_name__							:   Optional[int]=True      ,							__magic_name__							:   Optional[Any]=True      ,							__magic_name__							:   Optional[Any]=False      ,							__magic_name__							:   Optional[int]=False      ,							__magic_name__							:   str=False      ,							**__magic_name__							:   Dict      ,							)							->			int:
											lowerCamelCase_			:    Optional[Any]   						=      vocab_size
											lowerCamelCase_			:    Optional[int]   						=      n_positions
											lowerCamelCase_			:    Tuple   						=      n_embd
											lowerCamelCase_			:    List[str]   						=      n_layer
											lowerCamelCase_			:    List[Any]   						=      n_head
											lowerCamelCase_			:    str   						=      n_inner
											lowerCamelCase_			:    Any   						=      activation_function
											lowerCamelCase_			:    Any   						=      resid_pdrop
											lowerCamelCase_			:    List[Any]   						=      embd_pdrop
											lowerCamelCase_			:    Dict   						=      attn_pdrop
											lowerCamelCase_			:    str   						=      layer_norm_epsilon
											lowerCamelCase_			:    Dict   						=      initializer_range
											lowerCamelCase_			:    Optional[Any]   						=      scale_attn_weights
											lowerCamelCase_			:    int   						=      use_cache
											lowerCamelCase_			:    Union[str, Any]   						=      scale_attn_by_inverse_layer_idx
											lowerCamelCase_			:    int   						=      reorder_and_upcast_attn
											lowerCamelCase_			:    int   						=      tie_word_embeddings

											super().__init__(tie_word_embeddings=UpperCAmelCase_      ,							**UpperCAmelCase_							)





class 					snake_case_   (     _lowerCAmelCase	):



				'''simple docstring'''





				@property
				def    __SCREAMING_SNAKE_CASE     (      self							:   Union[str, Any]							)							->			Mapping[str, Mapping[int, str]]:
											return OrderedDict(
											    [
											        ("input_ids", {0: "batch", 1: "sequence"}),
											    ]							)





				def    __SCREAMING_SNAKE_CASE     (      self							:   Any      ,							__magic_name__							:   "FeatureExtractionMixin"      ,							__magic_name__							:   int = 1      ,							__magic_name__							:   int = -1      ,							__magic_name__							:   bool = False      ,							__magic_name__							:   Optional["TensorType"] = None      ,							__magic_name__							:   int = 3      ,							__magic_name__							:   int = 32      ,							__magic_name__							:   int = 32      ,							)							->			Mapping[str, Any]:
											lowerCamelCase_			:    Union[str, Any]   						=      self._generate_dummy_images(UpperCAmelCase_      ,							UpperCAmelCase_      ,							UpperCAmelCase_      ,							UpperCAmelCase_							)
											lowerCamelCase_			:    Any   						=      dict(preprocessor(images=UpperCAmelCase_      ,							return_tensors=UpperCAmelCase_							)							)

											return inputs



"""simple docstring"""


class 	__a	:






       def __init__(	self			:				Any							,   UpperCAmelCase_			:				List[str]							,   UpperCAmelCase_			:				Optional[Any]							,   UpperCAmelCase_			:				Optional[Any]      )->	Optional[int]:







              """simple docstring"""
              UpperCamelCase       			=		name
              UpperCamelCase       			=		value
              UpperCamelCase       			=		weight






       def __repr__(	self			:				int      )->	Any:







              """simple docstring"""
              return f"{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	Union[str, Any]:







              """simple docstring"""
              return self.value






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	int:







              """simple docstring"""
              return self.name






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple      )->	List[Any]:







              """simple docstring"""
              return self.weight






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any      )->	Union[str, Any]:







              """simple docstring"""
              return self.value / self.weight





def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				Union[str, Any]:







       """simple docstring"""
       UpperCamelCase       			=		[]
       for i in range(len(UpperCAmelCase_					)					):
              menu.append(Things(name[i]		,   value[i]		,   weight[i]					)					)
       return menu



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				Dict:







       """simple docstring"""
       UpperCamelCase       			=		sorted(UpperCAmelCase_		,   key=UpperCAmelCase_		,   reverse=UpperCAmelCase_					)
       UpperCamelCase       			=		[]
       UpperCamelCase			,							UpperCamelCase       			=		0.0, 0.0
       for i in range(len(UpperCAmelCase_					)					):
              if (total_cost + items_copy[i].get_weight()) <= max_cost:
                     result.append(items_copy[i]					)
                     total_cost += items_copy[i].get_weight()
                     total_value += items_copy[i].get_value()
       return (result, total_value)



def 						lowerCamelCase__      (				)->				Dict:







       """simple docstring"""


if __name__ == "__main__":
      import doctest

      doctest.testmod()





